Serve Autoscaling¶

This section should help you:

  • Understand how Ray Serve autoscaling works.

  • Learn how to configure the parameters for your workload.

Autoscaling architecture¶

Ray Serve’s autoscaling feature automatically increases or decreases a deployment’s number of replicas based on its load.


  • Each ServeHandle and each worker replica periodically push the stats to the autoscaler.

  • The autoscaler requires ServeHandle queue metrics and replicas queries metrics to make decision whether to scale (up or down) the replicas.

  • ServeHandle continues to poll the updated group of replicas from the controller. Upon discovery of the new replicas, it will send any buffered or new queries to the replica until max_concurrent_queries is reached


When the controller dies, the client will still be able to send queries, but autoscaling will be paused. When the controller recovers, the autoscaling will resume, but all previous metrics collected will be lost.

Autoscaling parameters¶

There are several parameters the autoscaling algorithm takes into consideration when deciding the target replicas for your deployment

min_replicas[default_value=1]: The minimal number of replicas for the deployment. min_replicas will also be the initial number of replicas when the deployment is deployed.


Ray Serve Autoscaling allows the min_replicas to be 0 when starting your deployment; the scale up will be started when you start sending traffic. There will be a cold start time as the Ray ServeHandle waits (blocks) for available replicas to assign the request.

max_replicas[default_value=1]: Max replicas is the maximum number of replicas for the deployment. Ray Serve Autoscaling will rely on the Ray Autoscaler to scale up more nodes when the currently available cluster resources (CPUs, GPUs, etc) are not enough to bring up more replicas.

target_num_ongoing_requests_per_replica[default_value=1]: The config is to maintain how many ongoing requests are expected to run concurrently per replica at most. If the number is lower, the scale up will be done more aggressively.


  • It is always recommended to load test your workloads. For example, if the use case is latency sensitive, you can lower the target_num_ongoing_requests_per_replica number to maintain high performance.

  • Internally, the autoscaler will compare RUNNING + PENNING tasks of each replicas and target_num_ongoing_requests_per_replica to decide scale up/down.

  • target_num_ongoing_requests_per_replica is only a target value used for autoscaling (not a hard limit), the real ongoing requests number can be higher than the config.

downscale_delay_s[default_value=600.0]: The config is to control how long the cluster needs to wait before scaling down replicas.

upscale_delay_s[default_value=30.0]: The config is to control how long the cluster need to wait before scaling up replicas.


downscale_delay_s and upscale_delay_s are to control the frequency of doing autoscaling work. E.g. if your use case takes a long time to do initialization work, you can increase downscale_delay_s to make the down scaling happen slowly.

smoothing_factor[default_value=1]: The multiplicative factor to speedup/slowdown each autoscaling step. E.g. When the use case has high large traffic volume in short period of time, you can increase smoothing_factor to scale up the resource quickly.

metrics_interval_s[default_value=10]: This is control how often each replica sends metrics to the autoscaler. (Normally you don’t need to change this config.)